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Record W2300191588 · doi:10.14288/1.0089760

Social responsibility in higher education : conducting a social audit of a community college

2009· article· en· W2300191588 on OpenAlexaff
J Holden

Bibliographic record

VenuecIRcle (University of British Columbia) · 2009
Typearticle
Languageen
FieldSocial Sciences
TopicService-Learning and Community Engagement
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsSocial responsibilityPublic relationsAuditHigher educationSociologyPolitical scienceBusinessAccountingLaw

Abstract

fetched live from OpenAlex

Social and ethical accounting, auditing and reporting (SEAAR) is a process with a range of tools and techniques that enables an organization to measure, account for, understand, report on and improve its social performance over time. Before 1993, British Columbia's post secondary education institutions implemented public policy and operated with a level of autonomy that enabled them to meet the needs of their students, employees and distinct communities. After 1993, public policy aimed at increased efficiency, effectiveness and accountability centralized major aspects of human resource and labour relations policy in British Columbia's public sector. This thesis describes the process, the results and the implications of conducting a social audit to assess the social performance of one particular community college. During the years 1991 - 2000, the College grew; diligently balanced its budgets through significant entrepreneurial efforts and employees remained committed to their students and the purpose of the College. However, as entrepreneurial efforts increased, it was apparent that the increased productivity had stretched the capacity of employees to deliver services and the consequences were evident. The use of sick leave increased with a concomitant increase in the incidence of short and long-term disability leaves. More employees reported feeling stressed and expressed less satisfaction about their work at the College. Social auditing is about accountability. Its stakeholder process complements traditional strategic planning processes. Within the context of a public sector organization, a process of social auditing, previously used in the private sector, was adapted to evaluate a broad range of organizational issues related to human resource policies and managerial practices. The College had a foundation of human resource policies, practices and programs that had achieved some of their purposes over time, and the social audit clearly identified where improvements where required. Key findings included the need to attend to workload and work design issues that were creating stress, the need to refocus performance evaluation, professional development and to enhance programs that recognized the contribution of employees. Training, particularly in the area of technological skills was recognized as critical for ensuring that employees were prepared for workplace changes. In particular, the social audit provided feedback on communication processes and identified improvements necessary to enhance open and transparent decision-making. Health and wellness programs were recognized as key to restoring balance to employees' work lives and reducing stress. A number of recommendations from the social audit were integrated into the College's Strategic Plan 2000 - 2003, and funds were allocated during the College's 2000 - 2001 budget process. In June 2000, the Board of Governors allocated additional funding to manage issues related to workload and stress that arose from the strategic plan and the social audit. Through a reflective process, the research enabled a human resource practitioner to develop and implement a process of social auditing, examine and understand the effects of centralized public policy on human resource management and labour relations policy in British Columbia's public sector and make recommendations for improvement to human resource policy in one of British Columbia's community colleges.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.805
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.072
GPT teacher head0.287
Teacher spread0.215 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1
Published2009
Admission routes1
Has abstractyes

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